#include "network.hpp"
#include "utils.hpp"
#include "parser.hpp"
#include "option_list.hpp"
#include "blas.hpp"
#include "assert.h"
#include "classifier.hpp"
#include "data.hpp"
#include "image.hpp"
#include "dark_cuda.hpp"

#include "darknet_internal.hpp"

#ifdef WIN32
#include <time.h>
#include "gettimeofday.h"
#else
#include <sys/time.h>
#endif

float validate_classifier_single(char *datacfg, char *filename, char *weightfile, network *existing_net, int topk_custom);

float *get_regression_values(char **labels, int n)
{
	float* v = (float*)xcalloc(n, sizeof(float));
	int i;
	for(i = 0; i < n; ++i){
		char *p = strchr(labels[i], ' ');
		*p = 0;
		v[i] = atof(p+1);
	}
	return v;
}

void train_classifier(char *datacfg, char *cfgfile, char *weightfile, int *gpus, int ngpus, int clear, int dontuse_opencv, int dont_show, int mjpeg_port, int calc_topk, int show_imgs, char* chart_path)
{
	int i;

	float avg_loss = -1;
	float avg_contrastive_acc = 0;
	char *base = basecfg(cfgfile);
	printf("%s\n", base);
	printf("%d\n", ngpus);
	network* nets = (network*)xcalloc(ngpus, sizeof(network));

	srand(time(0));
	int seed = rand();
	for(i = 0; i < ngpus; ++i){
		srand(seed);
#ifdef GPU
		cuda_set_device(gpus[i]);
#endif
		nets[i] = parse_network_cfg(cfgfile);
		if(weightfile){
			load_weights(&nets[i], weightfile);
		}
		if (clear) {
			*nets[i].seen = 0;
			*nets[i].cur_iteration = 0;
		}
		nets[i].learning_rate *= ngpus;
	}
	srand(time(0));
	network net = nets[0];

	int imgs = net.batch * net.subdivisions * ngpus;

	printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
	list *options = read_data_cfg(datacfg);

	char *backup_directory = option_find_str(options, "backup", "/backup/");
	char *label_list = option_find_str(options, "labels", "data/labels.list");
	char *train_list = option_find_str(options, "train", "data/train.list");
	int classes = option_find_int(options, "classes", 2);
	int topk_data = option_find_int(options, "top", 5);
	char topk_buff[10];
	sprintf(topk_buff, "top%d", topk_data);
	layer l = net.layers[net.n - 1];
	if (classes != l.outputs && (l.type == SOFTMAX || l.type == COST))
	{
		darknet_fatal_error(DARKNET_LOC, "num of filters = %d in the last conv-layer in cfg-file doesn't match to classes = %d in data-file", l.outputs, classes);
	}

	char **labels = get_labels(label_list);
	if (net.unsupervised) {
		free(labels);
		labels = NULL;
	}
	list *plist = get_paths(train_list);
	char **paths = (char **)list_to_array(plist);
	printf("%d\n", plist->size);
	int train_images_num = plist->size;
	clock_t time;

	load_args args = {0};
	args.w = net.w;
	args.h = net.h;
	args.c = net.c;
	args.threads = 32;
	if (net.contrastive && args.threads > net.batch/2) args.threads = net.batch / 2;
	args.hierarchy = net.hierarchy;

	args.contrastive = net.contrastive;
	args.dontuse_opencv = dontuse_opencv;
	args.min = net.min_crop;
	args.max = net.max_crop;
	args.flip = net.flip;
	args.blur = net.blur;
	args.angle = net.angle;
	args.aspect = net.aspect;
	args.exposure = net.exposure;
	args.saturation = net.saturation;
	args.hue = net.hue;
	args.size = net.w > net.h ? net.w : net.h;

	args.label_smooth_eps = net.label_smooth_eps;
	args.mixup = net.mixup;
	if (dont_show && show_imgs) show_imgs = 2;
	args.show_imgs = show_imgs;

	args.paths = paths;
	args.classes = classes;
	args.n = imgs;
	args.m = train_images_num;
	args.labels = labels;
	args.type = CLASSIFICATION_DATA;

#ifdef OPENCV
	//args.threads = 3;
	mat_cv* img = NULL;
	float max_img_loss = net.max_chart_loss;
	int number_of_lines = 100;
	int img_size = 1000;
	char windows_name[100];
	sprintf(windows_name, "chart_%s.png", base);
	if (!dontuse_opencv)
	{
		// This draws the initial blank chart.  Then see the call to update_train_loss_chart() below.
		img = draw_initial_train_chart(windows_name, max_img_loss, net.max_batches, number_of_lines, img_size, dont_show, chart_path);
	}
#endif  //OPENCV

	data train;
	data buffer;
	pthread_t load_thread;
	args.d = &buffer;
	load_thread = load_data(args);

	int iter_save = get_current_batch(net);
	int iter_save_last = get_current_batch(net);
	int iter_topk = get_current_batch(net);
	float topk = 0;

	//int count = 0;
	double start, time_remaining, avg_time = -1, alpha_time = 0.01;
	start = what_time_is_it_now();

	while(get_current_batch(net) < net.max_batches || net.max_batches == 0){
		time=clock();

		pthread_join(load_thread, 0);
		train = buffer;
		load_thread = load_data(args);

		printf("Loaded: %lf seconds\n", sec(clock()-time));
		time=clock();

		float loss = 0;
#ifdef GPU
		if(ngpus == 1){
			loss = train_network(net, train);
		} else {
			loss = train_networks(nets, ngpus, train, 4);
		}
#else
		loss = train_network(net, train);
#endif
		if(avg_loss == -1 || isnan(avg_loss) || isinf(avg_loss)) avg_loss = loss;
		avg_loss = avg_loss*.9 + loss*.1;

		i = get_current_batch(net);

		int calc_topk_for_each = iter_topk + 2 * train_images_num / (net.batch * net.subdivisions);  // calculate TOPk for each 2 Epochs
		calc_topk_for_each = fmax(calc_topk_for_each, net.burn_in);
		calc_topk_for_each = fmax(calc_topk_for_each, 100);
		if (i % 10 == 0) {
			if (calc_topk) {
				fprintf(stderr, "\n (next TOP%d calculation at %d iterations) ", topk_data, calc_topk_for_each);
				if (topk > 0) fprintf(stderr, " Last accuracy TOP%d = %2.2f %% \n", topk_data, topk * 100);
			}

			if (net.cudnn_half) {
				if (i < net.burn_in * 3) fprintf(stderr, " Tensor Cores are disabled until the first %d iterations are reached.\n", 3 * net.burn_in);
				else fprintf(stderr, " Tensor Cores are used.\n");
			}
		}

		int draw_precision = 0;
		if (calc_topk && (i >= calc_topk_for_each || i == net.max_batches)) {
			iter_topk = i;
			if (net.contrastive && l.type != SOFTMAX && l.type != COST) {
				int k;
				for (k = 0; k < net.n; ++k) if (net.layers[k].type == CONTRASTIVE) break;
				topk = *(net.layers[k].loss) / 100;
				sprintf(topk_buff, "Contr");
			}
			else {
				topk = validate_classifier_single(datacfg, cfgfile, weightfile, &net, topk_data); // calc TOP-n
				printf("\n accuracy %s = %f \n", topk_buff, topk);
			}
			draw_precision = 1;
		}

		time_remaining = ((net.max_batches - i) / ngpus) * (what_time_is_it_now() - start) / 60 / 60;
		// set initial value, even if resume training from 10000 iteration
		if (avg_time < 0) avg_time = time_remaining;
		else avg_time = alpha_time * time_remaining + (1 -  alpha_time) * avg_time;
		start = what_time_is_it_now();
		printf("%d, %.3f: %f, %f avg, %f rate, %lf seconds, %ld images, %f hours left\n", get_current_batch(net), (float)(*net.seen)/ train_images_num, loss, avg_loss, get_current_rate(net), sec(clock()-time), *net.seen, avg_time);
#ifdef OPENCV
		if (net.contrastive) {
			float cur_con_acc = -1;
			int k;
			for (k = 0; k < net.n; ++k)
				if (net.layers[k].type == CONTRASTIVE) cur_con_acc = *net.layers[k].loss;
			if (cur_con_acc >= 0) avg_contrastive_acc = avg_contrastive_acc*0.99 + cur_con_acc * 0.01;
			printf("  avg_contrastive_acc = %f \n", avg_contrastive_acc);
		}
		if (!dontuse_opencv)
		{
			update_train_loss_chart(windows_name, img, img_size, avg_loss, max_img_loss, i, net.max_batches, topk, draw_precision, topk_buff, avg_contrastive_acc / 100, dont_show, mjpeg_port, avg_time);
		}
#endif  // OPENCV

		if (i >= (iter_save + 1000)) {
			iter_save = i;
#ifdef GPU
			if (ngpus != 1) sync_nets(nets, ngpus, 0);
#endif
			char buff[256];
			sprintf(buff, "%s/%s_%d.weights", backup_directory, base, i);
			save_weights(net, buff);
		}

		if (i >= (iter_save_last + 100)) {
			iter_save_last = i;
#ifdef GPU
			if (ngpus != 1) sync_nets(nets, ngpus, 0);
#endif
			char buff[256];
			sprintf(buff, "%s/%s_last.weights", backup_directory, base);
			save_weights(net, buff);
		}
		free_data(train);
	}
#ifdef GPU
	if (ngpus != 1) sync_nets(nets, ngpus, 0);
#endif
	char buff[256];
	sprintf(buff, "%s/%s_final.weights", backup_directory, base);
	save_weights(net, buff);

#ifdef OPENCV
	release_mat(&img);
	destroy_all_windows_cv();
#endif

	pthread_join(load_thread, 0);
	free_data(buffer);

	//free_network(net);
	for (i = 0; i < ngpus; ++i) free_network(nets[i]);
	free(nets);

	//free_ptrs((void**)labels, classes);
	if(labels) free(labels);
	free_ptrs((void**)paths, plist->size);
	free_list(plist);
	free(base);

	free_list_contents_kvp(options);
	free_list(options);

}


/*
void train_classifier(char *datacfg, char *cfgfile, char *weightfile, int clear)
{
srand(time(0));
float avg_loss = -1;
char *base = basecfg(cfgfile);
printf("%s\n", base);
network net = parse_network_cfg(cfgfile);
if(weightfile){
load_weights(&net, weightfile);
}
if(clear) *net.seen = 0;

int imgs = net.batch * net.subdivisions;

printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
list *options = read_data_cfg(datacfg);

char *backup_directory = option_find_str(options, "backup", "/backup/");
char *label_list = option_find_str(options, "labels", "data/labels.list");
char *train_list = option_find_str(options, "train", "data/train.list");
int classes = option_find_int(options, "classes", 2);

char **labels = get_labels(label_list);
list *plist = get_paths(train_list);
char **paths = (char **)list_to_array(plist);
printf("%d\n", plist->size);
int N = plist->size;
clock_t time;

load_args args = {0};
args.w = net.w;
args.h = net.h;
args.threads = 8;

args.min = net.min_crop;
args.max = net.max_crop;
args.flip = net.flip;
args.angle = net.angle;
args.aspect = net.aspect;
args.exposure = net.exposure;
args.saturation = net.saturation;
args.hue = net.hue;
args.size = net.w;
args.hierarchy = net.hierarchy;

args.paths = paths;
args.classes = classes;
args.n = imgs;
args.m = N;
args.labels = labels;
args.type = CLASSIFICATION_DATA;

data train;
data buffer;
pthread_t load_thread;
args.d = &buffer;
load_thread = load_data(args);

int epoch = (*net.seen)/N;
while(get_current_batch(net) < net.max_batches || net.max_batches == 0){
time=clock();

pthread_join(load_thread, 0);
train = buffer;
load_thread = load_data(args);

printf("Loaded: %lf seconds\n", sec(clock()-time));
time=clock();

#ifdef OPENCV
if(0){
int u;
for(u = 0; u < imgs; ++u){
	image im = float_to_image(net.w, net.h, 3, train.X.vals[u]);
	show_image(im, "loaded");
	cvWaitKey(0);
}
}
#endif

float loss = train_network(net, train);
free_data(train);

if(avg_loss == -1) avg_loss = loss;
avg_loss = avg_loss*.9 + loss*.1;
printf("%d, %.3f: %f, %f avg, %f rate, %lf seconds, %d images\n", get_current_batch(net), (float)(*net.seen)/N, loss, avg_loss, get_current_rate(net), sec(clock()-time), *net.seen);
if(*net.seen/N > epoch){
	epoch = *net.seen/N;
	char buff[256];
	sprintf(buff, "%s/%s_%d.weights",backup_directory,base, epoch);
	save_weights(net, buff);
}
if(get_current_batch(net)%100 == 0){
	char buff[256];
	sprintf(buff, "%s/%s.backup",backup_directory,base);
	save_weights(net, buff);
}
}
char buff[256];
sprintf(buff, "%s/%s.weights", backup_directory, base);
save_weights(net, buff);

free_network(net);
free_ptrs((void**)labels, classes);
free_ptrs((void**)paths, plist->size);
free_list(plist);
free(base);
}
*/

void validate_classifier_crop(char *datacfg, char *filename, char *weightfile)
{
	int i = 0;
	network net = parse_network_cfg(filename);
	if(weightfile){
		load_weights(&net, weightfile);
	}
	srand(time(0));

	list *options = read_data_cfg(datacfg);

	char *label_list = option_find_str(options, "labels", "data/labels.list");
	char *valid_list = option_find_str(options, "valid", "data/train.list");
	int classes = option_find_int(options, "classes", 2);
	int topk = option_find_int(options, "top", 1);
	if (topk > classes) topk = classes;

	char **labels = get_labels(label_list);
	list *plist = get_paths(valid_list);

	char **paths = (char **)list_to_array(plist);
	int m = plist->size;
	free_list(plist);

	clock_t time;
	float avg_acc = 0;
	float avg_topk = 0;
	int splits = m/1000;
	int num = (i+1)*m/splits - i*m/splits;

	data val, buffer;

	load_args args = {0};
	args.w = net.w;
	args.h = net.h;

	args.paths = paths;
	args.classes = classes;
	args.n = num;
	args.m = 0;
	args.labels = labels;
	args.d = &buffer;
	args.type = OLD_CLASSIFICATION_DATA;

	pthread_t load_thread = load_data_in_thread(args);
	for(i = 1; i <= splits; ++i){
		time=clock();

		pthread_join(load_thread, 0);
		val = buffer;

		num = (i+1)*m/splits - i*m/splits;
		char **part = paths+(i*m/splits);
		if(i != splits){
			args.paths = part;
			load_thread = load_data_in_thread(args);
		}
		printf("Loaded: %d images in %lf seconds\n", val.X.rows, sec(clock()-time));

		time=clock();
		float *acc = network_accuracies(net, val, topk);
		avg_acc += acc[0];
		avg_topk += acc[1];
		printf("%d: top 1: %f, top %d: %f, %lf seconds, %d images\n", i, avg_acc/i, topk, avg_topk/i, sec(clock()-time), val.X.rows);
		free_data(val);
	}
}

void validate_classifier_10(char *datacfg, char *filename, char *weightfile)
{
	int i, j;
	network net = parse_network_cfg(filename);
	set_batch_network(&net, 1);
	if(weightfile){
		load_weights(&net, weightfile);
	}
	srand(time(0));

	list *options = read_data_cfg(datacfg);

	char *label_list = option_find_str(options, "labels", "data/labels.list");
	char *valid_list = option_find_str(options, "valid", "data/train.list");
	int classes = option_find_int(options, "classes", 2);
	int topk = option_find_int(options, "top", 1);
	if (topk > classes) topk = classes;

	char **labels = get_labels(label_list);
	list *plist = get_paths(valid_list);

	char **paths = (char **)list_to_array(plist);
	int m = plist->size;
	free_list(plist);

	float avg_acc = 0;
	float avg_topk = 0;
	int* indexes = (int*)xcalloc(topk, sizeof(int));

	for(i = 0; i < m; ++i){
		int class_id = -1;
		char *path = paths[i];
		for(j = 0; j < classes; ++j){
			if(strstr(path, labels[j])){
				class_id = j;
				break;
			}
		}
		int w = net.w;
		int h = net.h;
		int shift = 32;
		image im = load_image_color(paths[i], w+shift, h+shift);
		image images[10];
		images[0] = crop_image(im, -shift, -shift, w, h);
		images[1] = crop_image(im, shift, -shift, w, h);
		images[2] = crop_image(im, 0, 0, w, h);
		images[3] = crop_image(im, -shift, shift, w, h);
		images[4] = crop_image(im, shift, shift, w, h);
		flip_image(im);
		images[5] = crop_image(im, -shift, -shift, w, h);
		images[6] = crop_image(im, shift, -shift, w, h);
		images[7] = crop_image(im, 0, 0, w, h);
		images[8] = crop_image(im, -shift, shift, w, h);
		images[9] = crop_image(im, shift, shift, w, h);
		float* pred = (float*)xcalloc(classes, sizeof(float));
		for(j = 0; j < 10; ++j){
			float *p = network_predict(net, images[j].data);
			if(net.hierarchy) hierarchy_predictions(p, net.outputs, net.hierarchy, 1);
			axpy_cpu(classes, 1, p, 1, pred, 1);
			free_image(images[j]);
		}
		free_image(im);
		top_k(pred, classes, topk, indexes);
		free(pred);
		if(indexes[0] == class_id) avg_acc += 1;
		for(j = 0; j < topk; ++j){
			if(indexes[j] == class_id) avg_topk += 1;
		}

		printf("%d: top 1: %f, top %d: %f\n", i, avg_acc/(i+1), topk, avg_topk/(i+1));
	}
	free(indexes);
}

void validate_classifier_full(char *datacfg, char *filename, char *weightfile)
{
	int i, j;
	network net = parse_network_cfg(filename);
	set_batch_network(&net, 1);
	if(weightfile){
		load_weights(&net, weightfile);
	}
	srand(time(0));

	list *options = read_data_cfg(datacfg);

	char *label_list = option_find_str(options, "labels", "data/labels.list");
	char *valid_list = option_find_str(options, "valid", "data/train.list");
	int classes = option_find_int(options, "classes", 2);
	int topk = option_find_int(options, "top", 1);
	if (topk > classes) topk = classes;

	char **labels = get_labels(label_list);
	list *plist = get_paths(valid_list);

	char **paths = (char **)list_to_array(plist);
	int m = plist->size;
	free_list(plist);

	float avg_acc = 0;
	float avg_topk = 0;
	int* indexes = (int*)xcalloc(topk, sizeof(int));

	int size = net.w;
	for(i = 0; i < m; ++i){
		int class_id = -1;
		char *path = paths[i];
		for(j = 0; j < classes; ++j){
			if(strstr(path, labels[j])){
				class_id = j;
				break;
			}
		}
		image im = load_image_color(paths[i], 0, 0);
		image resized = resize_min(im, size);
		resize_network(&net, resized.w, resized.h);
		//show_image(im, "orig");
		//show_image(crop, "cropped");
		//cvWaitKey(0);
		float *pred = network_predict(net, resized.data);
		if(net.hierarchy) hierarchy_predictions(pred, net.outputs, net.hierarchy, 1);

		free_image(im);
		free_image(resized);
		top_k(pred, classes, topk, indexes);

		if(indexes[0] == class_id) avg_acc += 1;
		for(j = 0; j < topk; ++j){
			if(indexes[j] == class_id) avg_topk += 1;
		}

		printf("%d: top 1: %f, top %d: %f\n", i, avg_acc/(i+1), topk, avg_topk/(i+1));
	}
	free(indexes);
}


float validate_classifier_single(char *datacfg, char *filename, char *weightfile, network *existing_net, int topk_custom)
{
	int i, j;
	network net;
	int old_batch = -1;
	if (existing_net) {
		net = *existing_net;    // for validation during training
		old_batch = net.batch;
		set_batch_network(&net, 1);
	}
	else {
		net = parse_network_cfg_custom(filename, 1, 0);
		if (weightfile) {
			load_weights(&net, weightfile);
		}
		//set_batch_network(&net, 1);
		fuse_conv_batchnorm(net);
		calculate_binary_weights(net);
	}
	srand(time(0));

	list *options = read_data_cfg(datacfg);

	char *label_list = option_find_str(options, "labels", "data/labels.list");
	char *leaf_list = option_find_str(options, "leaves", 0);
	if(leaf_list) change_leaves(net.hierarchy, leaf_list);
	char *valid_list = option_find_str(options, "valid", "data/train.list");
	int classes = option_find_int(options, "classes", 2);
	int topk = option_find_int(options, "top", 1);
	if (topk_custom > 0) topk = topk_custom;    // for validation during training
	if (topk > classes) topk = classes;
	printf(" TOP calculation...\n");

	char **labels = get_labels(label_list);
	list *plist = get_paths(valid_list);

	char **paths = (char **)list_to_array(plist);
	int m = plist->size;
	free_list(plist);

	float avg_acc = 0;
	float avg_topk = 0;
	int* indexes = (int*)xcalloc(topk, sizeof(int));

	for(i = 0; i < m; ++i){
		int class_id = -1;
		char *path = paths[i];
		for(j = 0; j < classes; ++j){
			if(strstr(path, labels[j])){
				class_id = j;
				break;
			}
		}
		image im = load_image_color(paths[i], 0, 0);
		image resized = resize_min(im, net.w);
		image crop = crop_image(resized, (resized.w - net.w)/2, (resized.h - net.h)/2, net.w, net.h);
		//show_image(im, "orig");
		//show_image(crop, "cropped");
		//cvWaitKey(0);
		float *pred = network_predict(net, crop.data);
		if(net.hierarchy) hierarchy_predictions(pred, net.outputs, net.hierarchy, 1);

		if(resized.data != im.data) free_image(resized);
		free_image(im);
		free_image(crop);
		top_k(pred, classes, topk, indexes);

		if(indexes[0] == class_id) avg_acc += 1;
		for(j = 0; j < topk; ++j){
			if(indexes[j] == class_id) avg_topk += 1;
		}

		if (existing_net) printf("\r");
		else printf("\n");
		printf("%d: top 1: %f, top %d: %f", i, avg_acc/(i+1), topk, avg_topk/(i+1));
	}
	free(indexes);
	if (existing_net) {
		set_batch_network(&net, old_batch);
	}
	float topk_result = avg_topk / i;
	return topk_result;
}

void validate_classifier_multi(char *datacfg, char *filename, char *weightfile)
{
	int i, j;
	network net = parse_network_cfg(filename);
	set_batch_network(&net, 1);
	if(weightfile){
		load_weights(&net, weightfile);
	}
	srand(time(0));

	list *options = read_data_cfg(datacfg);

	char *label_list = option_find_str(options, "labels", "data/labels.list");
	char *valid_list = option_find_str(options, "valid", "data/train.list");
	int classes = option_find_int(options, "classes", 2);
	int topk = option_find_int(options, "top", 1);
	if (topk > classes) topk = classes;

	char **labels = get_labels(label_list);
	list *plist = get_paths(valid_list);
	int scales[] = {224, 288, 320, 352, 384};
	int nscales = sizeof(scales)/sizeof(scales[0]);

	char **paths = (char **)list_to_array(plist);
	int m = plist->size;
	free_list(plist);

	float avg_acc = 0;
	float avg_topk = 0;
	int* indexes = (int*)xcalloc(topk, sizeof(int));

	for(i = 0; i < m; ++i){
		int class_id = -1;
		char *path = paths[i];
		for(j = 0; j < classes; ++j){
			if(strstr(path, labels[j])){
				class_id = j;
				break;
			}
		}
		float* pred = (float*)xcalloc(classes, sizeof(float));
		image im = load_image_color(paths[i], 0, 0);
		for(j = 0; j < nscales; ++j){
			image r = resize_min(im, scales[j]);
			resize_network(&net, r.w, r.h);
			float *p = network_predict(net, r.data);
			if(net.hierarchy) hierarchy_predictions(p, net.outputs, net.hierarchy, 1);
			axpy_cpu(classes, 1, p, 1, pred, 1);
			flip_image(r);
			p = network_predict(net, r.data);
			axpy_cpu(classes, 1, p, 1, pred, 1);
			if(r.data != im.data) free_image(r);
		}
		free_image(im);
		top_k(pred, classes, topk, indexes);
		free(pred);
		if(indexes[0] == class_id) avg_acc += 1;
		for(j = 0; j < topk; ++j){
			if(indexes[j] == class_id) avg_topk += 1;
		}

		printf("%d: top 1: %f, top %d: %f\n", i, avg_acc/(i+1), topk, avg_topk/(i+1));
	}
	free(indexes);
}

void try_classifier(char *datacfg, char *cfgfile, char *weightfile, char *filename, int layer_num)
{
	network net = parse_network_cfg_custom(cfgfile, 1, 0);
	if(weightfile){
		load_weights(&net, weightfile);
	}
	set_batch_network(&net, 1);
	srand(2222222);

	list *options = read_data_cfg(datacfg);

	char *name_list = option_find_str(options, "names", 0);
	if(!name_list) name_list = option_find_str(options, "labels", "data/labels.list");
	int classes = option_find_int(options, "classes", 2);
	int top = option_find_int(options, "top", 1);
	if (top > classes) top = classes;

	char **names = get_labels(name_list);
	clock_t time;
	int* indexes = (int*)xcalloc(top, sizeof(int));
	char buff[256];
	char *input = buff;
	while(1){
		if(filename){
			strncpy(input, filename, 256);
		}else{
			printf("Enter Image Path: ");
			fflush(stdout);
			input = fgets(input, 256, stdin);
			if(!input) break;
			strtok(input, "\n");
		}
		image orig = load_image_color(input, 0, 0);
		image r = resize_min(orig, 256);
		image im = crop_image(r, (r.w - 224 - 1)/2 + 1, (r.h - 224 - 1)/2 + 1, 224, 224);
		float mean[] = {0.48263312050943, 0.45230225481413, 0.40099074308742};
		float std[] = {0.22590347483426, 0.22120921437787, 0.22103996251583};
		float var[3];
		var[0] = std[0]*std[0];
		var[1] = std[1]*std[1];
		var[2] = std[2]*std[2];

		normalize_cpu(im.data, mean, var, 1, 3, im.w*im.h);

		float *X = im.data;
		time=clock();
		float *predictions = network_predict(net, X);

		layer l = net.layers[layer_num];
		int i;
		for(i = 0; i < l.c; ++i){
			if(l.rolling_mean) printf("%f %f %f\n", l.rolling_mean[i], l.rolling_variance[i], l.scales[i]);
		}
#ifdef GPU
		cuda_pull_array(l.output_gpu, l.output, l.outputs);
#endif
		for(i = 0; i < l.outputs; ++i){
			printf("%f\n", l.output[i]);
		}
		/*

		printf("\n\nWeights\n");
		for(i = 0; i < l.n*l.size*l.size*l.c; ++i){
		printf("%f\n", l.filters[i]);
		}

		printf("\n\nBiases\n");
		for(i = 0; i < l.n; ++i){
		printf("%f\n", l.biases[i]);
		}
		*/

		top_predictions(net, top, indexes);
		printf("%s: Predicted in %f seconds.\n", input, sec(clock()-time));
		for(i = 0; i < top; ++i){
			int index = indexes[i];
			printf("%s: %f\n", names[index], predictions[index]);
		}
		free_image(im);
		if (filename) break;
	}
	free(indexes);
}

void predict_classifier(char *datacfg, char *cfgfile, char *weightfile, char *filename, int top)
{
	network net = parse_network_cfg_custom(cfgfile, 1, 0);
	if(weightfile){
		load_weights(&net, weightfile);
	}
	set_batch_network(&net, 1);
	srand(2222222);

	fuse_conv_batchnorm(net);
	calculate_binary_weights(net);

	list *options = read_data_cfg(datacfg);

	char *name_list = option_find_str(options, "names", 0);
	if(!name_list) name_list = option_find_str(options, "labels", "data/labels.list");
	int classes = option_find_int(options, "classes", 2);
	printf(" classes = %d, output in cfg = %d \n", classes, net.layers[net.n - 1].c);
	layer l = net.layers[net.n - 1];
	if (classes != l.outputs && (l.type == SOFTMAX || l.type == COST))
	{
		darknet_fatal_error(DARKNET_LOC, "num of filters = %d in the last conv-layer in cfg-file doesn't match to classes = %d in data-file", l.outputs, classes);
	}
	if (top == 0) top = option_find_int(options, "top", 1);
	if (top > classes) top = classes;

	int i = 0;
	char **names = get_labels(name_list);
	//clock_t time;
	int* indexes = (int*)xcalloc(top, sizeof(int));
	char buff[256];
	char *input = buff;
	//int size = net.w;
	while(1){
		if(filename){
			strncpy(input, filename, 256);
		}else{
			printf("Enter Image Path: ");
			fflush(stdout);
			input = fgets(input, 256, stdin);
			if(!input) break;
			strtok(input, "\n");
		}
		image im = load_image_color(input, 0, 0);
		image resized = resize_min(im, net.w);
		image cropped = crop_image(resized, (resized.w - net.w)/2, (resized.h - net.h)/2, net.w, net.h);
		printf("%d %d\n", cropped.w, cropped.h);

		float *X = cropped.data;

		double time = get_time_point();
		float *predictions = network_predict(net, X);
		printf("%s: Predicted in %lf milli-seconds.\n", input, ((double)get_time_point() - time) / 1000);

		if(net.hierarchy) hierarchy_predictions(predictions, net.outputs, net.hierarchy, 0);
		top_k(predictions, net.outputs, top, indexes);

		for(i = 0; i < top; ++i){
			int index = indexes[i];
			if(net.hierarchy) printf("%d, %s: %f, parent: %s \n",index, names[index], predictions[index], (net.hierarchy->parent[index] >= 0) ? names[net.hierarchy->parent[index]] : "Root");
			else printf("%s: %f\n",names[index], predictions[index]);
		}

		free_image(cropped);
		if (resized.data != im.data) {
			free_image(resized);
		}
		free_image(im);

		if (filename) break;
	}
	free(indexes);
	free_network(net);
	free_list_contents_kvp(options);
	free_list(options);
}


void label_classifier(char *datacfg, char *filename, char *weightfile)
{
	int i;
	network net = parse_network_cfg(filename);
	set_batch_network(&net, 1);
	if(weightfile){
		load_weights(&net, weightfile);
	}
	srand(time(0));

	list *options = read_data_cfg(datacfg);

	char *label_list = option_find_str(options, "names", "data/labels.list");
	char *test_list = option_find_str(options, "test", "data/train.list");
	int classes = option_find_int(options, "classes", 2);

	char **labels = get_labels(label_list);
	list *plist = get_paths(test_list);

	char **paths = (char **)list_to_array(plist);
	int m = plist->size;
	free_list(plist);

	for(i = 0; i < m; ++i){
		image im = load_image_color(paths[i], 0, 0);
		image resized = resize_min(im, net.w);
		image crop = crop_image(resized, (resized.w - net.w)/2, (resized.h - net.h)/2, net.w, net.h);
		float *pred = network_predict(net, crop.data);

		if(resized.data != im.data) free_image(resized);
		free_image(im);
		free_image(crop);
		int ind = max_index(pred, classes);

		printf("%s\n", labels[ind]);
	}
}


void test_classifier(char *datacfg, char *cfgfile, char *weightfile, int target_layer)
{
	int curr = 0;
	network net = parse_network_cfg(cfgfile);
	if(weightfile){
		load_weights(&net, weightfile);
	}
	srand(time(0));
	fuse_conv_batchnorm(net);
	calculate_binary_weights(net);

	list *options = read_data_cfg(datacfg);

	char *test_list = option_find_str(options, "test", "data/test.list");
	int classes = option_find_int(options, "classes", 2);

	list *plist = get_paths(test_list);

	char **paths = (char **)list_to_array(plist);
	int m = plist->size;
	free_list(plist);

	clock_t time;

	data val, buffer;

	load_args args = {0};
	args.w = net.w;
	args.h = net.h;
	args.paths = paths;
	args.classes = classes;
	args.n = net.batch;
	args.m = 0;
	args.labels = 0;
	args.d = &buffer;
	args.type = OLD_CLASSIFICATION_DATA;

	pthread_t load_thread = load_data_in_thread(args);
	for(curr = net.batch; curr < m; curr += net.batch){
		time=clock();

		pthread_join(load_thread, 0);
		val = buffer;

		if(curr < m){
			args.paths = paths + curr;
			if (curr + net.batch > m) args.n = m - curr;
			load_thread = load_data_in_thread(args);
		}
		fprintf(stderr, "Loaded: %d images in %lf seconds\n", val.X.rows, sec(clock()-time));

		time=clock();
		matrix pred = network_predict_data(net, val);

		int i, j;
		if (target_layer >= 0){
			//layer l = net.layers[target_layer];
		}

		for(i = 0; i < pred.rows; ++i){
			printf("%s", paths[curr-net.batch+i]);
			for(j = 0; j < pred.cols; ++j){
				printf("\t%g", pred.vals[i][j]);
			}
			printf("\n");
		}

		free_matrix(pred);

		fprintf(stderr, "%lf seconds, %d images, %d total\n", sec(clock()-time), val.X.rows, curr);
		free_data(val);
	}
}


void threat_classifier(char *datacfg, char *cfgfile, char *weightfile, int cam_index, const char *filename)
{
#ifdef OPENCV
	float threat = 0;
	float roll = .2;

	printf("Classifier Demo\n");
	network net = parse_network_cfg(cfgfile);
	if(weightfile){
		load_weights(&net, weightfile);
	}
	set_batch_network(&net, 1);
	list *options = read_data_cfg(datacfg);

	srand(2222222);
	cap_cv * cap;

	if (filename) {
		//cap = cvCaptureFromFile(filename);
		cap = get_capture_video_stream(filename);
	}
	else {
		//cap = cvCaptureFromCAM(cam_index);
		cap = get_capture_webcam(cam_index);
	}

	if(!cap)
	{
		darknet_fatal_error(DARKNET_LOC, "failed to connect to webcam (%d, %s)", cam_index, filename);
	}

	int classes = option_find_int(options, "classes", 2);
	int top = option_find_int(options, "top", 1);
	if (top > classes) top = classes;

	char *name_list = option_find_str(options, "names", 0);
	char **names = get_labels(name_list);

	int* indexes = (int*)xcalloc(top, sizeof(int));

	create_window_cv("Threat", 0, 512, 512);
	float fps = 0;
	int i;

	int count = 0;

	while(1){
		++count;
		struct timeval tval_before, tval_after, tval_result;
		gettimeofday(&tval_before, NULL);

		//image in = get_image_from_stream(cap);
		image in = get_image_from_stream_cpp(cap);
		if(!in.data) break;
		image in_s = resize_image(in, net.w, net.h);

		image out = in;
		int x1 = out.w / 20;
		int y1 = out.h / 20;
		int x2 = 2*x1;
		int y2 = out.h - out.h/20;

		int border = .01*out.h;
		int h = y2 - y1 - 2*border;
		int w = x2 - x1 - 2*border;

		float *predictions = network_predict(net, in_s.data);
		float curr_threat = 0;
		if(1){
			curr_threat = predictions[0] * 0 +
				predictions[1] * .6 +
				predictions[2];
		} else {
			curr_threat = predictions[218] +
				predictions[539] +
				predictions[540] +
				predictions[368] +
				predictions[369] +
				predictions[370];
		}
		threat = roll * curr_threat + (1-roll) * threat;

		draw_box_width(out, x2 + border, y1 + .02*h, x2 + .5 * w, y1 + .02*h + border, border, 0,0,0);
		if(threat > .97) {
			draw_box_width(out,  x2 + .5 * w + border,
					y1 + .02*h - 2*border,
					x2 + .5 * w + 6*border,
					y1 + .02*h + 3*border, 3*border, 1,0,0);
		}
		draw_box_width(out,  x2 + .5 * w + border,
				y1 + .02*h - 2*border,
				x2 + .5 * w + 6*border,
				y1 + .02*h + 3*border, .5*border, 0,0,0);
		draw_box_width(out, x2 + border, y1 + .42*h, x2 + .5 * w, y1 + .42*h + border, border, 0,0,0);
		if(threat > .57) {
			draw_box_width(out,  x2 + .5 * w + border,
					y1 + .42*h - 2*border,
					x2 + .5 * w + 6*border,
					y1 + .42*h + 3*border, 3*border, 1,1,0);
		}
		draw_box_width(out,  x2 + .5 * w + border,
				y1 + .42*h - 2*border,
				x2 + .5 * w + 6*border,
				y1 + .42*h + 3*border, .5*border, 0,0,0);

		draw_box_width(out, x1, y1, x2, y2, border, 0,0,0);
		for(i = 0; i < threat * h ; ++i){
			float ratio = (float) i / h;
			float r = (ratio < .5) ? (2*(ratio)) : 1;
			float g = (ratio < .5) ? 1 : 1 - 2*(ratio - .5);
			draw_box_width(out, x1 + border, y2 - border - i, x2 - border, y2 - border - i, 1, r, g, 0);
		}
		top_predictions(net, top, indexes);
		char buff[256];
		sprintf(buff, "tmp/threat_%06d", count);
		//save_image(out, buff);

#ifndef _WIN32
		printf("\033[2J");
		printf("\033[1;1H");
#endif
		printf("\nFPS:%.0f\n",fps);

		for(i = 0; i < top; ++i){
			int index = indexes[i];
			printf("%.1f%%: %s\n", predictions[index]*100, names[index]);
		}

		if(1){
			show_image(out, "Threat");
			wait_key_cv(10);
		}
		free_image(in_s);
		free_image(in);

		gettimeofday(&tval_after, NULL);
		timersub(&tval_after, &tval_before, &tval_result);
		float curr = 1000000.f/((long int)tval_result.tv_usec);
		fps = .9*fps + .1*curr;
	}
#endif
}


void gun_classifier(char *datacfg, char *cfgfile, char *weightfile, int cam_index, const char *filename)
{
#ifdef OPENCV_DISABLE
	int bad_cats[] = {218, 539, 540, 1213, 1501, 1742, 1911, 2415, 4348, 19223, 368, 369, 370, 1133, 1200, 1306, 2122, 2301, 2537, 2823, 3179, 3596, 3639, 4489, 5107, 5140, 5289, 6240, 6631, 6762, 7048, 7171, 7969, 7984, 7989, 8824, 8927, 9915, 10270, 10448, 13401, 15205, 18358, 18894, 18895, 19249, 19697};

	printf("Classifier Demo\n");
	network net = parse_network_cfg(cfgfile);
	if(weightfile){
		load_weights(&net, weightfile);
	}
	set_batch_network(&net, 1);
	list *options = read_data_cfg(datacfg);

	srand(2222222);
	CvCapture * cap;

	if (filename) {
		//cap = cvCaptureFromFile(filename);
		cap = get_capture_video_stream(filename);
	}
	else {
		//cap = cvCaptureFromCAM(cam_index);
		cap = get_capture_webcam(cam_index);
	}

	if(!cap)
	{
		darknet_fatal_error(DARKNET_LOC, "failed to connect to webcam (%d, %s)", cam_index, filename);
	}

	int classes = option_find_int(options, "classes", 2);
	int top = option_find_int(options, "top", 1);
	if (top > classes) top = classes;

	char *name_list = option_find_str(options, "names", 0);
	char **names = get_labels(name_list);

	int* indexes = (int*)xcalloc(top, sizeof(int));

	cvNamedWindow("Threat Detection", CV_WINDOW_NORMAL);
	cvResizeWindow("Threat Detection", 512, 512);
	float fps = 0;
	int i;

	while(1){
		struct timeval tval_before, tval_after, tval_result;
		gettimeofday(&tval_before, NULL);

		//image in = get_image_from_stream(cap);
		image in = get_image_from_stream_cpp(cap);
		image in_s = resize_image(in, net.w, net.h);
		show_image(in, "Threat Detection");

		float *predictions = network_predict(net, in_s.data);
		top_predictions(net, top, indexes);

		printf("\033[2J");
		printf("\033[1;1H");

		int threat = 0;
		for(i = 0; i < sizeof(bad_cats)/sizeof(bad_cats[0]); ++i){
			int index = bad_cats[i];
			if(predictions[index] > .01){
				printf("Threat Detected!\n");
				threat = 1;
				break;
			}
		}
		if(!threat) printf("Scanning...\n");
		for(i = 0; i < sizeof(bad_cats)/sizeof(bad_cats[0]); ++i){
			int index = bad_cats[i];
			if(predictions[index] > .01){
				printf("%s\n", names[index]);
			}
		}

		free_image(in_s);
		free_image(in);

		cvWaitKey(10);

		gettimeofday(&tval_after, NULL);
		timersub(&tval_after, &tval_before, &tval_result);
		float curr = 1000000.f/((long int)tval_result.tv_usec);
		fps = .9*fps + .1*curr;
	}
#endif
}

void demo_classifier(char *datacfg, char *cfgfile, char *weightfile, int cam_index, const char *filename, int benchmark, int benchmark_layers)
{
#ifdef OPENCV
	printf("Classifier Demo\n");
	network net = parse_network_cfg_custom(cfgfile, 1, 0);
	if(weightfile){
		load_weights(&net, weightfile);
	}
	net.benchmark_layers = benchmark_layers;
	set_batch_network(&net, 1);
	list *options = read_data_cfg(datacfg);

	fuse_conv_batchnorm(net);
	calculate_binary_weights(net);

	srand(2222222);
	cap_cv * cap;

	if(filename){
		cap = get_capture_video_stream(filename);
	}else{
		cap = get_capture_webcam(cam_index);
	}

	if(!cap)
	{
		darknet_fatal_error(DARKNET_LOC, "failed to connect to webcam (%d, %s)", cam_index, filename);
	}

	int classes = option_find_int(options, "classes", 2);
	int top = option_find_int(options, "top", 1);
	if (top > classes) top = classes;

	char *name_list = option_find_str(options, "names", 0);
	char **names = get_labels(name_list);

	int* indexes = (int*)xcalloc(top, sizeof(int));

	if (!benchmark) create_window_cv("Classifier", 0, 512, 512);
	float fps = 0;
	int i;

	double start_time = get_time_point();
	float avg_fps = 0;
	int frame_counter = 0;

	while(1){
		struct timeval tval_before;
		gettimeofday(&tval_before, NULL);

		//image in = get_image_from_stream(cap);
		image in_s, in;
		if (!benchmark) {
			in = get_image_from_stream_cpp(cap);
			in_s = resize_image(in, net.w, net.h);
			show_image(in, "Classifier");
		}
		else {
			static image tmp;
			if (!tmp.data) tmp = make_image(net.w, net.h, 3);
			in_s = tmp;
		}

		double time = get_time_point();
		float *predictions = network_predict(net, in_s.data);
		double frame_time_ms = (get_time_point() - time)/1000;
		frame_counter++;

		if(net.hierarchy) hierarchy_predictions(predictions, net.outputs, net.hierarchy, 1);
		top_predictions(net, top, indexes);

#ifndef _WIN32
		printf("\033[2J");
		printf("\033[1;1H");
#endif


		if (!benchmark) {
			printf("\rFPS: %.2f  (use -benchmark command line flag for correct measurement)\n", fps);
			for (i = 0; i < top; ++i) {
				int index = indexes[i];
				printf("%.1f%%: %s\n", predictions[index] * 100, names[index]);
			}
			printf("\n");

			free_image(in_s);
			free_image(in);

			int c = wait_key_cv(10);// cvWaitKey(10);
			if (c == 27 || c == 1048603) break;
		}
		else {
			printf("\rFPS: %.2f \t AVG_FPS = %.2f ", fps, avg_fps);
		}

		//gettimeofday(&tval_after, NULL);
		//timersub(&tval_after, &tval_before, &tval_result);
		//float curr = 1000000.f/((long int)tval_result.tv_usec);
		float curr = 1000.f / frame_time_ms;
		if (fps == 0) fps = curr;
		else fps = .9*fps + .1*curr;

		float spent_time = (get_time_point() - start_time) / 1000000;
		if (spent_time >= 3.0f) {
			//printf(" spent_time = %f \n", spent_time);
			avg_fps = frame_counter / spent_time;
			frame_counter = 0;
			start_time = get_time_point();
		}
	}
#endif
}


void run_classifier(int argc, char **argv)
{
	if(argc < 4){
		fprintf(stderr, "usage: %s %s [train/test/valid] [cfg] [weights (optional)]\n", argv[0], argv[1]);
		return;
	}

	int mjpeg_port = find_int_arg(argc, argv, "-mjpeg_port", -1);
	char *gpu_list = find_char_arg(argc, argv, "-gpus", 0);
	int *gpus = 0;
	int gpu = 0;
	int ngpus = 0;
	if(gpu_list){
		printf("%s\n", gpu_list);
		int len = strlen(gpu_list);
		ngpus = 1;
		int i;
		for(i = 0; i < len; ++i){
			if (gpu_list[i] == ',') ++ngpus;
		}
		gpus = (int*)xcalloc(ngpus, sizeof(int));
		for(i = 0; i < ngpus; ++i){
			gpus[i] = atoi(gpu_list);
			gpu_list = strchr(gpu_list, ',')+1;
		}
	} else {
		gpu = Darknet::CfgAndState::get().gpu_index;
		gpus = &gpu;
		ngpus = 1;
	}

	int dont_show = (Darknet::CfgAndState::get().is_shown ? 1 : 0);
//	int dont_show = find_arg(argc, argv, "-dont_show");
	int benchmark = find_arg(argc, argv, "-benchmark");
	int benchmark_layers = find_arg(argc, argv, "-benchmark_layers");
	if (benchmark_layers) benchmark = 1;
	int dontuse_opencv = find_arg(argc, argv, "-dontuse_opencv");
	int show_imgs = find_arg(argc, argv, "-show_imgs");
	int calc_topk = find_arg(argc, argv, "-topk");
	int cam_index = find_int_arg(argc, argv, "-c", 0);
	int top = find_int_arg(argc, argv, "-t", 0);
	int clear = find_arg(argc, argv, "-clear");
	char *data = argv[3];
	char *cfg = argv[4];
	char *weights = (argc > 5) ? argv[5] : 0;
	char *filename = (argc > 6) ? argv[6]: 0;
	char *layer_s = (argc > 7) ? argv[7]: 0;
	int layer = layer_s ? atoi(layer_s) : -1;
	char* chart_path = find_char_arg(argc, argv, "-chart", 0);
	if(0==strcmp(argv[2], "predict")) predict_classifier(data, cfg, weights, filename, top);
	else if(0==strcmp(argv[2], "try")) try_classifier(data, cfg, weights, filename, atoi(layer_s));
	else if(0==strcmp(argv[2], "train")) train_classifier(data, cfg, weights, gpus, ngpus, clear, dontuse_opencv, dont_show, mjpeg_port, calc_topk, show_imgs, chart_path);
	else if(0==strcmp(argv[2], "demo")) demo_classifier(data, cfg, weights, cam_index, filename, benchmark, benchmark_layers);
	else if(0==strcmp(argv[2], "gun")) gun_classifier(data, cfg, weights, cam_index, filename);
	else if(0==strcmp(argv[2], "threat")) threat_classifier(data, cfg, weights, cam_index, filename);
	else if(0==strcmp(argv[2], "test")) test_classifier(data, cfg, weights, layer);
	else if(0==strcmp(argv[2], "label")) label_classifier(data, cfg, weights);
	else if(0==strcmp(argv[2], "valid")) validate_classifier_single(data, cfg, weights, NULL, -1);
	else if(0==strcmp(argv[2], "validmulti")) validate_classifier_multi(data, cfg, weights);
	else if(0==strcmp(argv[2], "valid10")) validate_classifier_10(data, cfg, weights);
	else if(0==strcmp(argv[2], "validcrop")) validate_classifier_crop(data, cfg, weights);
	else if(0==strcmp(argv[2], "validfull")) validate_classifier_full(data, cfg, weights);

	if (gpus && gpu_list && ngpus > 1) free(gpus);
}
